{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1f20875-c50d-46df-906b-49ec1d0c6002",
   "metadata": {},
   "source": [
    "## 9.6 Acc和Loss曲线的绘制\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c90a18b9-d8e8-431c-856c-35f1e4ed05e1",
   "metadata": {},
   "source": [
    "### 1.任务描述\n",
    "\n",
    "训练手写数字识别网络，获取准确率和损失信息并可视化出来。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5b4fc39-cbcf-432a-bf1e-e75e642d4b87",
   "metadata": {},
   "source": [
    "### 2.知识准备\n",
    "\n",
    "见教程。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1d0295a-4ac4-470a-8263-027a3d69ac2c",
   "metadata": {},
   "source": [
    "### 3.任务分析\n",
    "\n",
    "可以使用如下代码将训练过程中的模型指标提取出来："
   ]
  },
  {
   "cell_type": "raw",
   "id": "beb7faea-e880-4a8d-9242-06f3c2403923",
   "metadata": {},
   "source": [
    "history = model.fit(…)\n",
    "acc = history.history['sparse_categorical_accuracy']\n",
    "val_acc = history.history['val_sparse_categorical_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c6090-cfda-4f46-a550-22a368e41e4a",
   "metadata": {},
   "source": [
    "### 4.任务实施\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec75eb6c-5da3-467d-a471-ca3b47242dd6",
   "metadata": {},
   "source": [
    "执行代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2ae9da58-e339-4d22-9f8d-ca255711d89e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "938/938 [==============================] - 4s 3ms/step - loss: 0.3041 - sparse_categorical_accuracy: 0.9146 - val_loss: 0.1668 - val_sparse_categorical_accuracy: 0.9502\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 2s 3ms/step - loss: 0.1388 - sparse_categorical_accuracy: 0.9598 - val_loss: 0.1163 - val_sparse_categorical_accuracy: 0.9662\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 3s 3ms/step - loss: 0.0962 - sparse_categorical_accuracy: 0.9714 - val_loss: 0.1049 - val_sparse_categorical_accuracy: 0.9687\n",
      "Epoch 4/5\n",
      "938/938 [==============================] - 3s 3ms/step - loss: 0.0735 - sparse_categorical_accuracy: 0.9778 - val_loss: 0.0863 - val_sparse_categorical_accuracy: 0.9749\n",
      "Epoch 5/5\n",
      "938/938 [==============================] - 3s 3ms/step - loss: 0.0588 - sparse_categorical_accuracy: 0.9824 - val_loss: 0.0828 - val_sparse_categorical_accuracy: 0.9753\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten (Flatten)           (None, 784)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               100480    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 加载数据\n",
    "(x_train, y_train), (x_test, y_test) = mnist = tf.keras.datasets.mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# 创建网络\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "# 配置网络\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
    "              metrics=['sparse_categorical_accuracy'])\n",
    "\n",
    "# 执行训练，返回history对象\n",
    "history = model.fit(\n",
    "    x_train, \n",
    "    y_train, \n",
    "    batch_size=64, \n",
    "    epochs=5, \n",
    "    validation_data=(x_test, y_test), \n",
    "    validation_freq=1)\n",
    "\n",
    "# 打印网络结构\n",
    "model.summary()\n",
    "\n",
    "# 显示训练集和测试集的Acc和Loss曲线\n",
    "acc = history.history['sparse_categorical_accuracy']\n",
    "val_acc = history.history['val_sparse_categorical_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "# 可视化准确率\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(acc, label='Training Accuracy')\n",
    "plt.plot(val_acc, '--', label='Validation Accuracy')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# 可视化损失\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(loss, label='Training Loss')\n",
    "plt.plot(val_loss, '--', label='Validation Loss')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6044c99-0741-4378-b2b6-f60c293cc3a9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
